sweelol/chronos-gemma-3-1b-v1
05
SWELOL Watch Editorial AI - Gemma-3-1B-it (FP16 LoRA)
Model Description
Production-ready luxury watch editorial generator fine-tuned on Gemma-3-1B-it using FP16 LoRA. Trained on 308 human-annotated watch descriptions with 0% hallucinations on brand/model specifications.
Created by: sweelol Version: 1.0 License: Apache 2.0 Training Loss: 0.27 Validation Pass Rate: 100%
Performance Metrics
Usage
⚠️ IMPORTANT: This model expects Gemma-3 turn-based prompt format
This model was fine-tuned using Gemma-3's native turn markers. Using the correct format is critical for optimal performance.
Correct Prompt Template:
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch
# Load model
tokenizer = AutoTokenizer.from_pretrained("google/gemma-3-1b-it")
base_model = AutoModelForCausalLM.from_pretrained(
"google/gemma-3-1b-it",
torch_dtype=torch.float16,
device_map="cuda:0"
)
model = PeftModel.from_pretrained(base_model, "sweelol/chronos-gemma-3-1b-v1")
# Build prompt with Gemma-3 turn markers
prompt = """<start_of_turn>user
Write a horological editorial in the style of A Collected Man based on these specifications.
Technical specifications:
Brand: Rolex, Model: Submariner, Reference: 126610LN, Case Size: 41mm
<end_of_turn>
<start_of_turn>model
"""
# Generate
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=500, temperature=0.4, top_p=0.9)
print(tokenizer.decode(outputs[0], skip_special_tokens=False))
bibtex
@software{sweelol_watch_editorial_ai_2026,
title = {Gemma-3-1B-it Watch Editorial Generator},
author = {sweelol},
year = {2026},
url = {https://huggingface.co/sweelol/chronos-gemma-3-1b-v1}
}